In this work, we propose a novel approach, termed AugBN, which has all the above properties of the Single Image Test-time Adaptation (SITA) setting.
The proposed method does not assume any access to the source data, adapts to one test instance at a time, and resets the model to the given source model for adapting to every new test instance.
The core of our method lies in calibrating the batch-norm statistics of the network.
Instead of using the source batch-norm statistics for testing, we calibrate it using the statistics from the individual test instances.
However, as estimating them from a single test instance is unreliable, we utilize certain augmentations of the test instance for this task.
Our adaptation method uses only one forward pass and thus is quite fast (comparable to using the source model directly), unlike TENT [1]} and TTT [2]}, which require at least one backward pass.
The main contributions of this work are summarised as follows:
